\chapter{Sermantic Aware Music Recommender Systems}



Examples of 
Core concepts related to the semantic web
RDF/S
SPARQL
Hands-on training of Sesame’s Workbench Web UI
Module 2: Linked Data

The linked data principles and associated best practices have significantly increased both the amount and the use of data in semantic form in recent years. Here, the principles are introduced and exemplified using significant data sets. This section includes an introduction to advanced technologies, such as OWL and SKOS, with an emphasis on their lightweight use in Linked Data. It also covers the VoiD vocabulary and foundational and developing work in the exposure of legacy sources, such as relational databases as Linked Data. It includes hands-on training with DBpedia.

Topics include:

Web technologies (HTTP, content negotiation)
Linked data principles
Best practices for publishing and interlinking (use of RDF, SPARQL, RDFS/OWL/SKOS, and VoiD)
Data consumption (including browsing, navigation, visualization)
Linked Open Data Cloud and selected examples
Hands-on learning of DBpedia
Module 3: Ontologies

Starting with a brief review of the origins and etymology of ontologies, participants will learn the ontology lifecycle and development processes. You will build an understanding of the terminology used by ontology engineers, explore the particulars of ontology modeling and learning, and be introduced to the most effective tools for creating ontologies. This section concludes with the presentation of several commonly used applications, including Protégé and domain ontologies.

Topics include:

Ontologies: History, types, properties, and applications
Ontology development from scratch
Ontology modeling principles
Examples of popular ontologies
Ontology population
Ontology development environments
Hands-on training with Protégé


%Formal ontology constitutes Mereolgy and Topology. Mereology is a study of whole-part relations which indicates when an object stops being itself and turns into another object. In heirarchal models, Mereology can be very handy to distinguish between essentials and non-essential components. Topology is the study of connections which indicates the strength of relationships and dependency between objects. There are several classifications of Computer Science’s ontologies, based on different parameters. In \cite{Guarino1998} classifies them by their level of generality in: top-level ontologies, which describe domain-independent concepts such as space, time, etc., and which are independent of specific problems;
%\begin{itemize} 
%\item Domain and task ontologies which describe the vocabulary related to a generic domain and a generic task, respectively.
%\item Application ontologies, which describe concepts depending on a particular domain and task.

%Van Heijst, Schereiber and Wieringa (1996) classify them according to their use in:
%\item terminological ontologies, which specify which terms are used to represent the knowledge;
%\item information ontologies, which specify storage structure data; and
%\item knowledge modeling ontologies, which specify the conceptualization of the knowledge.

%Fensel, (2004) classifies ontologies in:
%\item domain ontologies, which capture the knowledge valid for a particular domain;
%\item metadata ontologies, which provide a vocabulary for describing the content of on-line information sources;
%\item generic or common sense ontologies, which capture general knowledge about the world providing basic notions and concepts for things like time, space, state, event, etc;
%\item representational ontologies, that define the basic concepts for the representation of knowledge; and
%\item finally, method and particular tasks ontologies, which provide terms specific for particular tasks and methods. They provide a reasoning point of view on domain knowledge. 

%Gómez-Perez, Fernández-López and Corcho (2003) classify ontology based on the level of specification of relationships among the terms gathered on the ontology, in:
%\item Lightweight ontologies, which include concepts, concept taxonomies, relationships between concepts and properties that describe concepts.
%\item Heavyweight ontologies which add axioms and constraints to lightweight ontologies. Those axioms and constraints clarify the intended meaning of the terms involved into the ontology.
%\end{itemize}

davies2003towards
http://www.acm.org/class, http://www.dmoz.org/, http://www.swebok.org

